高效液相色谱法测定人血浆中阿莫西林浓度及药代动力学

高效液相色谱法测定人血浆中阿莫西林浓度及药代动力学谭力周继红罗楠袁倚盛（南京军区南京总医院中心仪器分析科，南京２１０００１）阿莫西林（ａｍｏｘｉｃｉｌｉｎ）为β内酰胺类抗生素，其抗菌谱广，口服受食物影响小，对大多数病人耐受性良好，因而在临床上得以广泛...     

抗胆碱药3-(2-苯基-2-环戊基-2-羟基-乙氧基)奎宁环烷的立体化学和构效关系

研究了强效抗胆碱药dl-3-(2-苯基-2-环戊基-2-羟基-乙氧基)-奎宁环烷的四个光学异构体的两种不对称合成方法,用HPLC检测了异构体含量,讨论了构效关系。     

Treatment of severe heart failure: quantity or quality of life? A trial of enoximone. Enoximone Investigators.

OBJECTIVES--To determine the effects of enoximone on mortality and quality of life in patients with severe end stage heart failure. DESIGN--A randomised, double blind, placebo controlled trial of the addition of enoximone to conventional treatment. Planned minimum follow up of one year. SETTING--District general hospitals and cardiological referral centres in the United Kingdom. PATIENTS--Planned 200 patients with severe, symptomatic heart failure despite treatment with diuretics and where appropriate and tolerated angiotensin converting enzyme inhibitors and digoxin. RESULTS--The study was ended early by the ethics committee after 151 patients had been recruited because of an excess mortality in the enoximone group: 27 deaths compared with 18 in the placebo group (P < 0.05). Quality of life measured with a disease specific questionnaire showed a clinically significant improvement at week 2 with a mean increase score of 0.48 in the enoximone treated patients compared with 0.14 in those receiving placebo (P = 0.0086). With the Nottingham health profile questionnaire the physical mobility score was improved after three months in the enoximone group, median 21.3 compared with 41.8 in the placebo group (P = 0.008). CONCLUSIONS--In patients with severe heart failure who remain incapacitated despite conventional treatment enoximone reduced survival but had a beneficial effect on the quality of life. Drugs that improve symptoms in severe end stage heart failure should not be discarded lightly.     

A latent class model for repeated measurements experiments.

Standard models for the analysis of repeated measurements assume a common response profile for all experimental units within a treatment group. However, in many applications this under-represents the nature of the response. There may be several distinct modes of response within a group (for example, responders versus non-responders to a given treatment), or there may be a set of distinct response profiles which are common to all the treatment groups. In these situations the effect of treatment can be characterized both by the shape of the fitted profiles and by estimating the proportion of cases who exhibit each particular response profile. This paper describes how such experiments may be analysed through the introduction of a latent variable into the standard model. Maximum likelihood estimation is straight-forward using the EM algorithm. Model choice requires some care, but good-fitting models can be identified via inspection of residuals and the use of empirical semi-variogram plots. Once the number of distinct profiles has been determined, treatment effects can be investigated using likelihood-ratio statistics. The approach is illustrated with a re-analysis of a dataset first described by Grizzle and Allen.